Abstract: Paidamoyo Chapfuwa's (Duke University, Expected 2021) research focuses on bringing modern machine learning approaches to survival analysis, i.e, causal inference, generative modeling, and Bayesian nonparametric. In particular, Paidamoyo's work examines generative methods for high-performance (accurate, calibrated, uncertainty-aware predictions) survival models. Moreover, her work introduces an adversarial distribution matching approach and a novel covariate-conditional Kaplan-Meier estimator, accounting for the predictive uncertainty in survival model calibration. In addition, her work also enables an interpretable time-to-event driven clustering method using a Bayesian nonparametric stick-breaking representation of the Dirichlet Process that represents patients in a clustered latent space. Recently, Paidamoyo’s work has explored a unified framework for individualized treatment effect estimation for survival outcomes from observation data.